SQL Practice Guide for the Google Data Analytics Certificate (2026)

The Google Data Analytics Certificate uses SQL across data preparation, cleaning, and analysis rather than treating it as one isolated unit. The official course pages show BigQuery practice with filtering, sorting, string cleaning, joins, COUNT DISTINCT, aggregations, and subqueries. You need to explain what a query returns, not merely recognize syntax.

There is no separate Google Data Analytics certification exam. SQL is assessed through course quizzes, assignments, and hands-on activities. Practice in the same direction as the curriculum: start with a business question, write a query, inspect the result, and verify that the result actually answers the question.

Where does SQL appear in the certificate?

The Prepare Data for Exploration course introduces databases, extracting and organizing data, filtering, sorting, BigQuery, and SQL best practices. Process Data from Dirty to Clean applies SQL functions to string cleaning and transformation, then verifies and documents the result. Analyze Data to Answer Questions uses joins, aliases, COUNT DISTINCT, aggregations, and subqueries to combine and analyze multiple tables.

That sequence matters. SQL in the certificate is not only SELECT practice: it supports the Prepare, Process, and Analyze phases of the broader data-analysis process.

What SQL query order should you memorize?

A reliable starting pattern is SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT. Not every query uses every clause, but their written order should remain consistent.

• SELECT: choose the columns or calculations returned.
• FROM: identify the source table.
• WHERE: filter individual rows before aggregation.
• GROUP BY: create groups for aggregate calculations.
• HAVING: filter the completed groups.
• ORDER BY: sort the result.
• LIMIT: restrict output rows. LIMIT alone does not necessarily reduce bytes scanned or query cost in BigQuery; inspect the query estimate and use the course practice environment.

Worked query: SELECT region, COUNT(*) AS order_count, SUM(revenue) AS total_revenue FROM orders WHERE order_date >= DATE '2026-01-01' AND order_date < DATE '2027-01-01' GROUP BY region HAVING SUM(revenue) > 10000 ORDER BY total_revenue DESC;

This query first keeps 2026 orders, groups them by region, calculates order count and revenue, removes regions at or below $10,000, and sorts the remaining regions by revenue. Reading that sequence aloud is a useful self-check.

What is the difference between WHERE and HAVING?

WHERE filters rows before grouping; HAVING filters groups after aggregation. Use WHERE for conditions such as region = 'West' or order_date after a cutoff. Use HAVING for conditions involving aggregate results, such as SUM(revenue) greater than 10,000.

Suppose the question is “Which product categories had at least 100 orders?” The category counts do not exist until SQL groups the rows, so the condition belongs in HAVING: SELECT category, COUNT(*) AS orders FROM sales GROUP BY category HAVING COUNT(*) >= 100;

How do COUNT, NULL, and DISTINCT interact?

• COUNT(*): counts rows, including rows with NULL in a particular column.
• COUNT(customer_id): counts only rows where customer_id is not NULL.
• COUNT(DISTINCT customer_id): counts unique non-NULL customer IDs.

If an orders table contains 1,000 rows, 20 missing customer IDs, and 700 unique identified customers, those expressions return 1,000, 980, and 700 respectively. Choosing the wrong count can change a business conclusion even though the query runs successfully.

How do you clean text fields with SQL?

The cleaning course explicitly includes SQL functions for string variables. Common operations include removing outer spaces, standardizing case, replacing inconsistent values, checking length, and converting types. Function names differ slightly across SQL dialects, so read BigQuery documentation and the course examples rather than assuming every database behaves identically.

Worked cleaning query: SELECT customer_id, LOWER(TRIM(email)) AS clean_email FROM customers WHERE email IS NOT NULL;

TRIM removes extra outer whitespace, LOWER standardizes case, and the WHERE clause excludes missing email values. Before updating a source table, compare the original and cleaned values in a SELECT result and document the transformation rule.

How do INNER JOIN and LEFT JOIN change the result?

INNER JOIN keeps only matching keys from both tables. LEFT JOIN keeps every row from the left table and adds matching data from the right table; unmatched right-side columns become NULL.

Assume customers contains 500 customer IDs and orders contains purchases for 320 of them. An INNER JOIN from customers to orders excludes customers with no order. A LEFT JOIN preserves all 500 customers, which makes it suitable for finding people who have not purchased.

Matched-order query: SELECT c.customer_id, c.customer_name, o.order_id FROM customers AS c INNER JOIN orders AS o ON c.customer_id = o.customer_id;

No-order query: SELECT c.customer_id, c.customer_name FROM customers AS c LEFT JOIN orders AS o ON c.customer_id = o.customer_id WHERE o.order_id IS NULL;

The second query uses the NULL created by an unmatched LEFT JOIN to isolate customers with no corresponding order.

When should you use a subquery?

A subquery calculates or filters an intermediate result that the outer query then uses. The certificate's analysis course includes queries within queries and subqueries for aggregation.

To find orders above the overall average, calculate the average inside the WHERE condition: SELECT order_id, order_total FROM orders WHERE order_total > (SELECT AVG(order_total) FROM orders);

The inner query returns one number. The outer query compares every order with that number. Before writing a subquery, state what the inner result must be: a single value, a list, or a table. That prediction prevents many shape and comparison errors.

How should you practice in BigQuery?

• Use the BigQuery sandbox or the setup path described in the course.
• Preview the schema and a small number of rows before selecting every column.
• Write down the expected grain: one row per order, customer, day, or region.
• Run a base count before and after a join to detect accidental duplication.
• Use aliases that describe the calculation, such as total_revenue or unique_customers.
• Check NULL values explicitly rather than assuming every key is populated.
• Save a clean version of each query with a one-sentence business question.

BigQuery uses GoogleSQL (formerly called Standard SQL), but other systems may differ in date functions, casting, string functions, and whether an alias can be used in a later clause. The transferable skill is understanding the operation, then checking the dialect.

What are the most common SQL mistakes in the certificate?

• Using HAVING for a row-level condition or WHERE for an aggregate condition.
• Selecting a non-aggregated column that is missing from GROUP BY.
• Turning a LEFT JOIN into an effective INNER JOIN by filtering right-side values incorrectly.
• Using COUNT(column) when missing values should still count as rows.
• Joining tables on a non-unique field and multiplying rows unexpectedly.
• Reading a query result without checking whether the output grain matches the question.
• Cleaning values without recording the original field and transformation rule.

When a query fails, reduce it to SELECT and FROM, confirm the table and columns, then add one clause at a time. When it runs but looks wrong, compare row counts, unique keys, NULL counts, and a handful of known records.

A seven-session SQL practice sequence

• Session 1: SELECT, aliases, LIMIT, and reading a schema.
• Session 2: WHERE with dates, numbers, text, NULL, AND, and OR.
• Session 3: COUNT, SUM, AVG, GROUP BY, HAVING, and ORDER BY.
• Session 4: TRIM, case normalization, casting, and a cleaning changelog.
• Session 5: INNER JOIN and LEFT JOIN with pre- and post-join row counts.
• Session 6: subqueries and COUNT DISTINCT.
• Session 7: one business question combining a filter, join, aggregate, and written recommendation.

Use the data-cleaning guide for complementary spreadsheet and validation practice, and review the current certificate structure before planning around older R-based materials. The GDA Complete Study Guide PDF includes six SQL labs with 38 runnable named queries. The 242-page expanded edition also provides current Python practice and companion datasets, code, formulas, and expected outputs. The optional R reference is labeled legacy.

Verification note: Curriculum topics and tools were verified against the official Coursera pages for Prepare Data for Exploration, Process Data from Dirty to Clean, and Analyze Data to Answer Questions on October 14, 2026.

Practice the explanation, not only the syntax

Take the free GDA skills quiz, then rewrite each missed SQL concept as a business question and a query. The SimpuTech Google Data Analytics study coach can turn the seven-session sequence into a schedule that matches your available time.

Run a complete synthetic practice query

This GoogleSQL query defines its own three-row input, so it does not require a private dataset. Predict the answer before running it. East has two completed orders totaling $100; West has one cancelled order that the filter excludes. The result is one row: East, 2, 100.

WITH orders AS (
  SELECT 'A1' AS order_id, 'East' AS region,
         'complete' AS status, 40 AS amount
  UNION ALL SELECT 'A2', 'East', 'complete', 60
  UNION ALL SELECT 'A3', 'West', 'cancelled', 20
)
SELECT region, COUNT(*) AS order_count, SUM(amount) AS revenue
FROM orders
WHERE status = 'complete'
GROUP BY region
ORDER BY region;

Now change A3 to complete. The output should include West, 1, 20 as a second row. Then add a second A2 row and observe that the query counts it again: SQL cannot know it is an accidental duplicate without a grain and deduplication rule. This exercise connects syntax to validation rather than treating a successful query run as proof of a correct business result.